Capital One

Data Engineer 4 (Manager, IC)

$179K–$225K

Richmond, VA +1 · Manager · Full-time · Posted Oct 1, 2026

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Location: Richmond, VA
McLean, VA
Experience: At least 4 years in application development, 2 years in distributed data, SQL, one programming language, and data pipeline design and development; 1 year in data modeling and end-to-end data solutions. Preferred experience ranges from 2+ to 7+ years, depending on area.
Education: Bachelor's Degree or higher in Computer Science or a related quantitative field.
Visa sponsorship: Capital One will not sponsor a new applicant for employment authorization or offer immigration-related support for this position.

About the job

Build and operate scalable cloud data pipelines and platforms for Risk management, including proprietary AI-powered solutions.

What you'll do

Design, build, test, deploy, and support cloud-first data solutions, pipelines, and platforms.
Set data engineering patterns and implement scalable, resilient, secure systems.
Collaborate across Agile teams and communicate technical concepts and data outcomes to stakeholders.

What they ask for

Bachelor's Degree or higher in Computer Science or a related quantitative field.
At least 4 years of application development experience; internship experience does not apply.
At least 2 years of experience in distributed data, SQL, a programming language (Python, Java, or Scala), and data pipeline design and development.
At least 1 year of experience in data modeling and end-to-end data solutions using relational and non-relational database systems.

Nice to have

7+ years of application development experience with proficiency in Python, SQL, Scala, or Java.
Experience with public cloud, distributed data workloads, streaming pipelines, observability or orchestration, NoSQL, data warehousing, Agile, or reusable data products.

Pay

$197,300 - $225,100 USD base salary (McLean, VA); $179,400 - $204,700 USD base salary (Richmond, VA)

Tools

Python, Spark, SQL, Java, Scala, Databricks, Snowflake, Airflow

AI focus

Build and deploy proprietary Risk management solutions powered by AI; the team also uses machine learning to prevent risks and automatically detect issues.

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